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Identification of Hammerstein-Wiener model with discontinuous input nonlinearity

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Abstract

This paper deals with the identification of Hammerstein-Wiener models with an irregular function in the input block. These models comprise a set of linear segments. The linear time invariant (LTI) block may be parametric or nonparametric. The nonlinearity of the output can be any continuous function of arbitrary shape; it is not necessarily assumed to be invertible. Subsequently, sine inputs will be used to identify the system parameters. First, the output nonlinearity is determined by filtering the system output and varying the phase of a given reference sine signal; second, the parameters of the linear block can be determined for any frequency; and finally, the input nonlinearity can be obtained by changing the offset and amplitude of the input sine signal.

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References

  1. Brouri A. Wiener-Hammerstein nonlinear system identification using spectral analysis. Int J Robust Nonlinear Control, 2022, 32: 6184–6204

    Article  MathSciNet  Google Scholar 

  2. Castro-Garcia R, Agudelo O M, Suykens J A K. Impulse response constrained LS-SVM modelling for MIMO Hammerstein system identification. Int J Control, 2019, 92: 908–925

    Article  MathSciNet  MATH  Google Scholar 

  3. Schoukens M, Tiels K. Identification of block-oriented nonlinear systems starting from linear approximations: a survey. Automatica, 2017, 85: 272–292

    Article  MathSciNet  MATH  Google Scholar 

  4. Brouri A, Kadi L. Identification of nonlinear systems. In: Proceedings of Conference SIAM CT’19, Chengdu, 2019. 22–24

  5. Brouri A, Kadi L, Slassi S. Frequency identification of Hammerstein-Wiener systems with backlash input nonlinearity. Int J Control Autom Syst, 2017, 15: 2222–2232

    Article  Google Scholar 

  6. Hsu K, Poolla K, Vincent T L. Identification of structured nonlinear systems. IEEE Trans Automat Contr, 2008, 53: 2497–2513

    Article  MathSciNet  MATH  Google Scholar 

  7. Novara C, Vincent T, Hsu K, et al. Parametric identification of structured nonlinear systems. Automatica, 2011, 47: 711–721

    Article  MathSciNet  MATH  Google Scholar 

  8. Chen H F. Recursive identification for Wiener model with discontinuous piece-wise linear function. IEEE Trans Automat Contr, 2006, 51: 390–400

    Article  MathSciNet  MATH  Google Scholar 

  9. Brouri A, Kadi L, Benyassi M. Identification of nonlinear systems having discontinuous nonlinearity. Int J Modelling Identif Control, 2019, 33: 130–137

    Article  Google Scholar 

  10. Giri F, Rochdi Y, Brouri A, et al. Parameter identification of Hammerstein systems containing backlash operators with arbitrary-shape parametric borders. Automatica, 2011, 47: 1827–1833

    Article  MathSciNet  MATH  Google Scholar 

  11. Giri F, Rochdi Y, Radouane A, et al. Frequency identification of nonparametric Wiener systems containing backlash nonlinearities. Automatica, 2013, 49: 124–137

    Article  MathSciNet  MATH  Google Scholar 

  12. Giri F, Radouane A, Brouri A, et al. Combined frequency-prediction error identification approach for Wiener systems with backlash and backlash-inverse operators. Automatica, 2014, 50: 768–783

    Article  MathSciNet  MATH  Google Scholar 

  13. Palanthandalam-Madapusi H J, Ridley A J, Bernstein D S. Identification and prediction of ionospheric dynamics using a Hammerstein-Wiener model with radial basis functions. In: Proceedings of the American Control Conference, Portland, 2005. 5052–5057

  14. Taringou F, Hammi O, Srinivasan B, et al. Behaviour modelling of wideband RF transmitters using Hammerstein-Wiener models. IET Circuits Devices Syst, 2010, 4: 282–290

    Article  Google Scholar 

  15. Ouannou A, Giri F, Brouri A, et al. Parameter identification of switched reluctance motor using exponential swept-sine signal. IFAC-PapersOnLine, 2022, 55: 132–137

    Article  Google Scholar 

  16. Śliwiński P. Nonlinear System Identification by Haar Wavelets. Berlin: Springer-Verlag, 2013. 210

    Book  MATH  Google Scholar 

  17. Bai E W. Identification of linear systems with hard input nonlinearities of known structure. Automatica, 2002, 38: 853–860

    Article  MathSciNet  MATH  Google Scholar 

  18. Brouri A, Ouannou A, Giri F, et al. Identification of parallel Wiener-Hammerstein systems. IFAC-PapersOnLine, 2022, 55: 25–30

    Article  Google Scholar 

  19. Brouri A, Kadi L, Lahdachi K. Identification of nonlinear system composed of parallel coupling of Wiener and Hammerstein models. Asian J Control, 2022, 24: 1152–1164

    Article  MathSciNet  Google Scholar 

  20. Wills A, Schön T B, Ljung L, et al. Identification of Hammerstein-Wiener models. Automatica, 2013, 49: 70–81

    Article  MathSciNet  MATH  Google Scholar 

  21. Brouri A, Chaoui F Z, Giri F. Identification of Hammerstein-Wiener models with hysteresis front nonlinearities. Int J Control, 2022, 95: 3353–3367

    Article  MathSciNet  MATH  Google Scholar 

  22. Brouri A, Giri F, Ikhouane F, et al. Identification of Hammerstein-Wiener systems with backlash input nonlinearity bordered by straight lines. IFAC Proc Volumes, 2014, 47: 475–480

    Article  Google Scholar 

  23. Giri F, Brouri A, Amdouri O, et al. Frequency identification of Hammerstein-Wiener systems with piecewise affine input nonlinearity. IFAC Proc Volumes, 2014, 47: 10030–10035

    Article  Google Scholar 

  24. Ni B, Gilson M, Garnier H. Refined instrumental variable method for Hammerstein-Wiener continuous-time model identification. IET Control Theor Appl, 2013, 7: 1276–1286

    Article  MathSciNet  Google Scholar 

  25. Schoukens M, Bai E, Rolain Y. Identification of Hammerstein-Wiener systems. In: Proceedings of the 16th IFAC Symposium on System Identification, 2012. 274–279

  26. Wang D, Ding F. Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems. Comput Math Appl, 2008, 56: 3157–3164

    Article  MathSciNet  MATH  Google Scholar 

  27. Vörös J. An iterative method for Hammerstein-Wiener systems parameter identification. Int J Control, 2010, 83: 1117–1124

    Article  MATH  Google Scholar 

  28. Cerone V, Razza V, Regruto D. One-shot set-membership identification of generalized Hammerstein-Wiener systems. Automatica, 2020, 118: 109028

    Article  MathSciNet  MATH  Google Scholar 

  29. Vincent T L, Novara C. Mixed parametric/non-parametric identification of systems with discontinuous nonlinearities. Automatica, 2013, 49: 3661–3669

    Article  MathSciNet  MATH  Google Scholar 

  30. Li F, Jia L. Parameter estimation of Hammerstein-Wiener nonlinear system with noise using special test signals. Neurocomputing, 2019, 344: 37–48

    Article  Google Scholar 

  31. Mzyk G, Biegański M, Mielcarek P. Multi-level identification of Hammerstein-Wiener systems. IFAC-PapersOnLine, 2019, 52: 174–179

    Article  MathSciNet  Google Scholar 

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Correspondence to F. Z. El Mansouri.

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Brouri, A., El Mansouri, F.Z., Chaoui, F.Z. et al. Identification of Hammerstein-Wiener model with discontinuous input nonlinearity. Sci. China Inf. Sci. 66, 222201 (2023). https://doi.org/10.1007/s11432-022-3767-2

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  • DOI: https://doi.org/10.1007/s11432-022-3767-2

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